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Spike SortingCluster CuttingRemap2pin02 SpikesSelected FeaturesFeaturesFeatures PlotSlide 7Training Features PlotSlide 9Slide 10Future DirectionConclusionSpike Sorting•Goal: Extract neural spike trains from MEA electrode data•Method 1: Convolution of template spikes•Method 2: Sort by spikes featuresCluster Cutting•Advantages: –Better separation–Requires less information•Disadvantages–Computationally intensiveRemap2pin02 SpikesSelected Features1. Max peak height2. Voltage difference between max and second max3. Sum of max positive and max negative peaks 4. Time between max positive and max negative peaks 5. Max width of a polarizationFeatures1. Max peak height -- Color2. Voltage difference between max and second max -- Z-axis3. Sum of max positive and max negative peaks -- Y-axis4. Time between max positive and max negative peaks -- X-axis 5. Max width of a polarization -- SizeFeatures PlotRemap2pin02 SpikesTraining Features PlotTraining Features PlotTraining Features PlotFuture Direction•Optimal feature choice•Training algorithm–Bayesian clustering–Nearest neighbor–Support Vector Machine–Neural NetworkConclusion•Data suggests we should be able to isolate individual neural firing patterns from MEA data•Use MEA data to model and study network of neurons in


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MIT 9 29 - Spike Sorting

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